Hyperspectral Image Analysis Based on Color Channels and Ensemble Classifier

Hyperspectral image analysis is a dynamically developing branch of computer vision due to the numerous practical applications and high complexity of data. There exist a need for introducing novel machine learning methods, that can tackle high dimensionality and large number of classes in these images. In this paper, we introduce a novel ensemble method for classification of hyperspectral data. The pool of classifiers is built on the basis of color decomposition of the given image. Each base classifier corresponds to a single color channel that is extracted. We propose a new method for decomposing hyperspectral image into 11 different color channels. As not all of the channels may bear as useful information as other, we need to promote the most relevant ones. For this, our ensemble uses a weighted trained fuser, which uses a neural methods for establishing weights. We show, that the proposed ensemble can outperform other state-of-the-art classifiers in the given task.